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STAgent

Skill FridrichMethod/awesome-skills/skills/spatial-transcriptomics-analysis/STAgent

Spatial analystFrom its SKILL.md

Install
npx -y skills add FridrichMethod/awesome-skills --skill STAgent

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SKILL.md

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<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

Spatial Transcriptomics Agent

Run STAgent to align histology images with expression matrices, perform clustering/SVG detection, and generate literature-backed spatial reports.

When to Use

  • Analysis of Visium/Xenium or similar ST datasets.
  • Visual reasoning over spatial plots, H&E images, or cluster maps.
  • Automatically generating Scanpy/Squidpy code for new ST workflows.
  • Hypothesis generation about spatial gene expression patterns.

Core Capabilities

  1. Dynamic code generation: Create/execute Python scripts for QC, clustering, SVG detection.
  2. Visual reasoning: Interpret spatial plots to identify tissue domains and cell neighborhoods.
  3. Literature retrieval: Pull references that contextualize findings.
  4. Report generation: Deliver publication-style writeups with plots and SVG tables.

Workflow

  1. Env setup: conda env create -f environment.yml && conda activate STAgent.
  2. Data prep: Supply expression_path (.h5ad/Spaceranger) + image_path (H&E/IF) and metadata.
  3. Task selection: Choose tasks such as cluster, find_svg, annotate_domains, or composite instructions; run python repo/src/main.py --data_path ... --task "...".
  4. Execute & interpret: Let STAgent generate scripts, run analyses, and interpret results with literature references.
  5. Package outputs: Save UMAP/spatial plots, SVG tables, QC details, and summary markdown.

Example Usage

User: "Analyze this breast cancer ST dataset, find immune infiltrates."
Agent: loads data, runs `sqidpy.gr.spatial_neighbors`, computes Leiden clusters, plots marker genes (CD3D, CD19), and summarizes which clusters map to tumor core vs. stromal/immune zones.

Guardrails

  • Document coordinate systems and any scaling between imaging and expression coordinates.
  • Avoid definitive cell-type labels without supporting markers.
  • Capture QC parameters for reproducibility.

References

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

What ships with it: 54 files

8140.4 KB alongside SKILL.md, 10 of them executable

repo/

14 more files not listed here. See all 54 in the repository.

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